*       token attempt, one measure of the quality of a hash function is <i>how well</i> it succeeds
 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden

 *       token attempt, one measure of the quality of a hash function is <i>how well</i> it succeeds
 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden

Para manter as coisas simples, aqui nós estamos apenas adicionando os escopos recebidos diretamente ao token.

Porém em sua aplicação, por segurança, você deve garantir que você apenas adiciona os escopos que o usuário possui permissão de fato, ou aqueles que você predefiniu.

///

{* ../../docs_src/security/tutorial005_an_py310.py hl[157] *}

## Declare escopos em *operações de rota* e dependências { #declare-scopes-in-path-operations-and-dependencies }

    }

    /**
     *
     */
    public static class Bean3 {

        /**
         *
         */
        public Date aaa;

    }

    /**
     * @author kato
     */
    public static class BeanNames {

        /**
         * CharSequenceを作成します。
         *
         * @param name
         * @return CharSequence
         */

pkg math/big, method (*Float) Parse(string, int) (*Float, int, error)
pkg math/big, method (*Float) Prec() uint
pkg math/big, method (*Float) Quo(*Float, *Float) *Float
pkg math/big, method (*Float) Rat(*Rat) (*Rat, Accuracy)
pkg math/big, method (*Float) Set(*Float) *Float
pkg math/big, method (*Float) SetFloat64(float64) *Float
pkg math/big, method (*Float) SetInf(bool) *Float
pkg math/big, method (*Float) SetInt(*Int) *Float

          assertThat(prob).isWithin(epsilon).of(0.50d);
        }
      }
    }
  }

  /**
   * Checks that a Hasher returns the same HashCode when given the same input, and also that the
   * collision rate looks sane.
   */
  static void assertInvariants(HashFunction hashFunction) {
    int objects = 100;
    Set<HashCode> hashcodes = Sets.newHashSetWithExpectedSize(objects);
    Random random = new Random(314159);

# CL 101750048 math: implement Nextafter32, Robert Griesemer <******@****.***>
pkg math, func Nextafter32(float32, float32) float32

# CL 93550043 math/big: implement Rat.Float32, Robert Griesemer <******@****.***>
pkg math/big, method (*Rat) Float32() (float32, bool)

# CL 76540043 net/http: add BasicAuth method to *http.Request, Kelsey Hightower <******@****.***>

    Closer closer = Closer.create();
    try {
      InputStream in = closer.register(openStream());
      return countBySkipping(in);
    } catch (IOException e) {
      // skip may not be supported... at any rate, try reading
    } finally {
      closer.close();
    }

    closer = Closer.create();
    try {
      InputStream in = closer.register(openStream());
      return ByteStreams.exhaust(in);

Isso, somado ao simples fato que Python é a principal linguagem para **Data Science**, Aprendizado de Máquina e especialmente Deep Learning, faz do FastAPI uma ótima escolha para APIs web e aplicações com Data Science / Aprendizado de Máquina (entre muitas outras).

   *
   * <p>See http://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives for the
   * formula.
   *
   * @param n expected insertions (must be positive)
   * @param p false positive rate (must be 0 < p < 1)
   */
  @VisibleForTesting
  static long optimalNumOfBits(long n, double p) {
    if (p == 0) {
      p = Double.MIN_VALUE;
    }
    return (long) (-n * Math.log(p) / SQUARED_LOG_TWO);